The present invention relates generally to estimating the value of a real estate entity, and more particularly, to combining house price forecasts.
Financial institutions and businesses involved with sales of real estate have long tried to asses the value of real estate entities accurately. For example, financial institutions use estimated value of the real estate entity as one of the key factors in approving loan applications for real estate sales. Relying on the soundness of the estimate, financial institutions accept the risk of lending large sums of money and attach the property as security for the transaction. In this sense, the accuracy of estimated value of the real estate entity is critical.
In addition to the accuracy of the estimate, timeliness is a significant factor. For example, closing on a real estate sales contract may depend on the buyer successfully obtaining a loan within a limited time period. Hence, it is important for lenders to be able to estimate the value of the real estate entity quickly.
Traditionally, real estate personnel performed appraisals manually, but this poses many problems. First, manual appraisals are subjective and vary depending on the appraiser. Second, manual appraisals are expensive. Third, manual appraisals may not be timely due to many unpredictable conditions such as appraiser availability, scheduling conflicts, and weather conditions.
Some have tried to automate the real estate valuation process. For example, Jost et al., U.S. Pat. No. 5,361,201, discloses a neural network-based system for automated real estate valuation. It also discusses other efforts and problems with using statistical models to value real estate entities. In its discussion, Jost et al. points out deficiencies of traditional statistical techniques in estimating property values, namely the inability to capture the complexity and the changing trend of the data. It also discusses difficulties involved with selecting a proper sample size for a statistical model to achieve an acceptable stability and reliability of the estimate.
Jost et al., however, did not explore combining predictive;models, including statistical models, to predict values of real estate entities. Some studies on general forecasting techniques show combining the results of individual models may produce a significantly better estimate than each individual estimate, but none examined the problems of the real estate market. Moreover, there have been no studies to automate the valuation of real estate by combining the predictive models.
Therefore, it is desirable to increase the accuracy of real estate value forecasts by combining the results of each constituent models.
It is also desirable to provide a timely and reliable estimate of value that is free of human biases and inconsistency.
The present invention combines house price forecasts to obviate the limitations and disadvantages of the related art.
In accordance with the purpose of the present invention, gas embodied and broadly described, a method of estimating the value of a real estate entity comprises several steps. A data processor accesses real estate data and a plurality of predictive models. The processor forms a plurality of estimates for the value of the real estate entity based on the predictive models and selects a plurality of best estimates according to a predetermined criteria. The processor also converts the best estimates into weighted estimates and combines the weighted estimates into a final estimate.
According to another aspect of the present invention, a system comprises a database, forming means, selecting means, converting means, and a combining means. The database contains real estate data and a plurality of predictive models. The forming means forms a plurality of estimates for the value of the real estate entity based on the predictive models. The selecting means selects a plurality of best estimates. The converting means converts the best estimates into weighted estimates according to the predetermined criteria and, finally, the combining means combines the weighted estimates into a final estimate.